Secure and Energy-Efficient Communication for Internet of Drones Networks: A Deep Reinforcement Learning Approach

Internet of Drones (IoD)-aided wireless networks are proving their efficiency in various commercial and military applications, such as object recognition, surveillance, and data acquisition. However, the broadcast communication nature of IoD networks raises significant communication security issues. This paper investigates drone-to-ground communication subject to eavesdroppers in urban environments. We aim to provide secure communication utilizing physical layer security by increasing network secrecy rates. In addition, we aim to reduce the energy consumption within the IoD network by optimizing drones' transmitting and jamming power and employing energy harvesting techniques to charge drones wirelessly. Our optimization problem is formulated as a Markov decision process (MDP), and a deep reinforcement learning (DRL) algorithm is proposed to solve the problem.